A Novel Method for Groups Identification Based on Spatio-Temporal Trajectories

With the rapid development of sensing hard-devices, wireless communication technologies and smart mobile devices, a large number of data for moving objects have been collected, among which a group of high precision data (e.g., GPS) are widely used for traffic predictions and management. However, in modern city life, a large volume of positioning data of moving objects is collected with low-precision positions, which causes the difficulty for trajectory match, analysis or group identification. In view of this limitation, this paper proposes a novel method for the semantic trajectory based group identification. Specifically, the trajectory data are used to discover the spatial and semantic information of persons to calculate their similarities. Based on which, the groups of persons with strong correlations are identified. To evaluate our method, we conduct several experiments on Geolife dataset. The experimental results show that the proposed method has a significant effect on the group identification.